- author = "Dominic Jones and Hongbo Wang and Ali Alazmani and Peter R. Culmer",
- title = "A soft multi-axial force sensor to assess tissue properties in RealTime",
- booktitle = "2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
- year = "2017",
- pages = "5738--5743",
- address = "Vancouver, Canada",
- month = "24-28 " # sep,
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/IROS.2017.8206464",
- size = "6 pages",
-
abstract = "Objective: This work presents a method for the use of
a soft multi-axis force sensor to determine tissue
trauma in Minimally Invasive Surgery.
Despite recent developments, there is a lack of effective haptic sensing technology employed in instruments for Minimally Invasive Surgery (MIS). There is thus a clear clinical need to increase the provision of haptic feedback and to perform real-time analysis of haptic data to inform the surgical operator.
This paper establishes a methodology for the capture of real-time data through use of an inexpensive prototype grasper. Fabricated using soft silicone and 3D printing, the sensor is able to precisely detect compressive and shear forces applied to the grasper face. The sensor is based upon a magnetic soft tactile sensor, using variations in the local magnetic field to determine force.
The performance of the sensing element is assessed and a linear response was observed, with a max hysteresis error of 4.1percent of the maximum range of the sensor. To assess the potential of the sensor for surgical sensing, a simulated grasping study was conducted using ex vivo porcine tissue. Two previously established metrics for prediction of tissue trauma were obtained and compared from recorded data. The normalized stress rate (kPa.mm -1 ) of compression and the normalized stress relaxation (Delta rho R) were analysed across repeated grasps. The sensor was able to obtain measures in agreement with previous research, demonstrating future potential for this approach.
In summary this work demonstrates that inexpensive soft sensing systems can be used to instrument surgical tools and thus assess properties such as tissue health. This could help reduce surgical error and thus benefit patients.",
-
notes = "p5739 'The system was calibrated using a Genetic
Programming algorithm to define a discrete relationship
between force and magnetic field'
also known as \cite{8206464}
School of Mechanical Engineering, University of Leeds",
